Deep learning in spiking neural networks
Amirhossein Tavanaei,Masoud Ghodrati,Saeed Reza Kheradpisheh,Timothée Masquelier,Anthony S. Maida +4 more
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TLDR
The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.About:
This article is published in Neural Networks.The article was published on 2019-03-01 and is currently open access. It has received 756 citations till now. The article focuses on the topics: Spiking neural network & Artificial neural network.read more
Citations
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An overview of neuromorphic computing for artificial intelligence enabled hardware-based hopfield neural network
TL;DR: This paper presents a comprehensive review and focuses extensively on the Hopfield algorithm’s model and its potential advancement in new research applications to facilitate developers with a better understanding of the aforementioned model in accordance to build their own artificial intelligence projects.
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Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions
Halit Apaydin,Mohammad Taghi Sattari,Mohammad Taghi Sattari,Kambiz Falsafian,Ramendra Prasad +4 more
TL;DR: In this paper, the authors investigated the potential of Singular Spectral Analysis (SSA), Seasonal-Trend decomposition using Loess (STL) and attribute selection pre-processing approaches with the neural network methods in predicting monthly river streamflows in the Nallihan stream, Turkey.
Journal ArticleDOI
Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems
Journal ArticleDOI
Unsupervised speech recognition through spike-timing-dependent plasticity in a convolutional spiking neural network
Meng Dong,Xuhui Huang,Bo Xu +2 more
TL;DR: It is demonstrated that an STDP-based convolutional SNN model equipped with local weight sharing and temporal coding is capable of solving the SR task accurately and efficiently.
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Locally connected spiking neural networks for unsupervised feature learning.
TL;DR: A method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plasticity (STDP) rule, which has the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning.
References
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Proceedings ArticleDOI
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
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